AI/ML Engineer

We are looking for an AI Engineer who has solid experience in RAG, LangChain and LlamaIndex. Our candidate is an engineer who takes ownership and immerses him/herself into the work.

Responsibilities
Develop and optimize AI-driven solutions: Design, implement, and refine AI systems that leverage Retrieval-Augmented Generation (RAG), embeddings, and multi-model architectures to solve complex business challenges.
Integrate and deploy multi-modal AI models: Work with multi-modal AI models (text, image, etc.) and tools like LangChain, LangGraph, and LlamaIndex to create comprehensive solutions that meet diverse user needs.
Conduct prompt engineering: Design, test, and refine prompts to enhance the performance and reliability of AI models in various applications, ensuring they deliver accurate and contextually relevant results, implement evaluations for the prompts.
Leverage creative AI tools: Utilize and integrate AI tools such as MidJourney and Stable Diffusion for generating and enhancing visual content, supporting diverse AI-driven projects.
Optimize model performance: Apply advanced techniques such as embeddings, transfer learning, and the use of specialized tools to fine-tune models, improving their accuracy, efficiency, and scalability.
Stay updated on AI advancements: Continuously monitor and assess the latest trends, tools, and research in AI, particularly in areas like RAG, embeddings, multi-models, and tools like LangChain, LangGraph, and LlamaIndex, to incorporate cutting-edge techniques into projects.
Ensure ethical AI deployment: Implement best practices in AI ethics, ensuring models are fair, transparent, and free from biases that could negatively impact users.
Document and communicate AI solutions: Clearly document the design, implementation, and optimization of AI systems, and effectively communicate these to technical and non-technical stakeholders.
Troubleshoot and refine AI systems: Identify and resolve issues in AI models and pipelines, ensuring robust performance across different scenarios and applications.

Qualifications
Hands-on experience in AI/ML development.
Proficiency in programming languages such as Python, TensorFlow, PyTorch, or other relevant frameworks.
Strong knowledge of AI/ML algorithms and architectures, particularly in multi-modal and retrieval-augmented generation models.
Experience with AI deployment and integration into production systems.
Familiarity with AI ethics, including fairness, transparency, and bias mitigation strategies.
Strong troubleshooting skills with the ability to identify and resolve issues in AI models and pipelines.
Proven experience in developing and optimizing AI systems, specifically using Retrieval-Augmented Generation (RAG), embeddings, and multi-modal architectures.
Experience with multi-modal AI models (e.g., text, image) and proficiency in tools like LangChain, LangGraph, and LlamaIndex.
Demonstrated experience in prompt engineering, including designing, testing, and refining prompts.
Experience with creative AI tools such as MidJourney and Stable Diffusion for visual content generation.
Strong experience in model optimization techniques, including embeddings, transfer learning, and fine-tuning models for performance improvements.